Classifying Amazon Reviews with Python: From Raw Text to 88% Accuracy

Ever wondered how businesses know if customers are happy or not? In this project, I built a machine learning model that classifies Amazon product reviews as Positive or Negative using NLP techniques. Here's how I did it. The Dataset I used the Amazon Review Polarity Dataset — sampling 200,000 reviews for training and 50,000 for testing. The dataset was perfectly balanced between positive and negative reviews, which is ideal for classification. Cleaning the Text Raw reviews are messy. I wrote a preprocessing function to lowercase text, strip punctuation, numbers, and remove stopwords using NLTK. This is really helpful for the model to identify words properly. def clean_text(text): text = str(text).lower() text = re.sub(r"[^\w\s]", "", text) text = re.sub(r"\d+", "", text) words = [word for word in text.split() if word not in stop_words] return " ".join(words) Converting Text to Numbers with TF-IDF Machine learning models need numbers, not words. TF-IDF weighs words by how unique they ar